Overity.ai - MLOps oriented framework for trustworthy Edge-AI
🚧 Overity.ai is currently under heavy development 🚧
Overity.ai is a open-source MLOps oriented framework for optimization, qualification, and deployment of Edge-AI models in critical systems, focusing on traceability and trustworthy AI.
It helps you organize training, optimization and validation methods, track and version your datasets and models with complete traceability graphs, as well as leveraging Hardware-In-The-Loop (HIL) testing to validate embedded use-cases.
Whether it's for local development or complex deployments in complete MLOps environments, Overity.ai provides all the necessary tools to create the next ground-breaking Edge-AI Revoluuuuuuuution! :smile:
Getting started
To get started, please refer to the Getting started guide.
Contributing
If you want to contribute, please refer to the contributing guidelines.
License
This project is licensed under the terms of the terms of the Apache 2.0 license. Please refer to the LICENSE file for more information.
Main authors
- Florian Dupeyron (florian.dupeyron@mugcat.fr): Main project author and leader
1 maintainer
Metadata
1 maintainer
Release files for overity 0.0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| overity-0.0.7.tar.gz | 517.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| overity-0.0.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 625.3 kB
Release files / overity-0.0.7.tar.gz
| Download URL | overity-0.0.7.tar.gz |
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| Size | 517.7 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
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twine/6.1.0 CPython/3.13.12
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Transparency logRelease files / overity-0.0.7-py3-none-any.whl
| Download URL | overity-0.0.7-py3-none-any.whl |
|---|---|
| Size | 107.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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